The softmax classifier is likely to be biased towards the classes with more training samples [1]}, [2]}, [3]}. In the online continual learning scenario, the learning agent accesses more samples related to new classes because only a small fraction of the old examples are stored. Thus, the model tends to classify samples into new classes. To tackle such an issue, as every column of the weight matrix \(W\)  for the softmax classifier represents the weights for the corresponding class (hence being related to the degree of bias),
we propose weight-aware balanced sampling (WABS) which adaptively decides the sample ratio between old and new classes to balance the classifier based on degree of bias.
We first define a sampling rate \(\gamma \)  as follows:
\( \gamma =\min {(1,\frac{2\times \text{exp}(w_{\text{old}}/\tau _w)}{\text{exp}(w_{\text{old}}/\tau _w)+\text{exp}(w_{\text{new}}/\tau _w)})}\) 
